{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/Meiyun-Tan/kkbAssignment/blob/master/BI04%E5%90%8D%E4%BC%81%E7%8F%AD/MQ_BI04_lesson04_Action01_ImageSVD.ipynb\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from scipy.linalg import svd # svd奇异值分解\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6048, 4024, 3)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = Image.open(\"D:/me/保龄球1.jpg\")\n",
    "\n",
    "im_gray = img.convert('L')\n",
    "img_arr = np.array(img)\n",
    "img_arr.shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "显示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img_arr, cmap=plt.cm.gray, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im_gray, cmap=plt.cm.gray, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 SVD奇异值分解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "im_gray_arr = np.array(im_gray)\n",
    "\n",
    "p, s, q = svd(im_gray_arr, \n",
    "              full_matrices=False) #If False,the shapes are `(M, K)` and `(K, N)`,where `K = min(M, N)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 取前K个特征，对图像进行还原\n",
    "def get_image_feature(p, q, s, k):\n",
    "    \"取Top-k个特征对图像进行还原\"\n",
    "    temp_s = np.zeros(s.shape[0])\n",
    "    temp_s[:k] = s[:k]\n",
    "    s = temp_s * np.identity(s.shape[0])\n",
    "    temp = np.dot(p, s)\n",
    "    temp = np.dot(temp, q)\n",
    "    plt.imshow(temp, cmap=plt.cm.gray, interpolation='nearest')\n",
    "    plt.title(\"k=%d\" % k)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    print(np.square(im_gray_arr - temp).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel1_arr = img_arr[:, :, 0]\n",
    "channel2_arr = img_arr[:, :, 1]\n",
    "channel3_arr = img_arr[:, :, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1, s1, q1 = svd(channel1_arr, full_matrices=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = int(s1.shape[0] * 0.5)\n",
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13240349625.659084\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Image data of dtype object cannot be converted to float",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-573afe60ca72>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mchannel1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_image_feature\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mq1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchannel1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'nearest'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"channel:1 k=%d\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'off'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, data, **kwargs)\u001b[0m\n\u001b[0;32m   2643\u001b[0m         \u001b[0mfilterrad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4.0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimlim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdeprecation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_deprecated_parameter\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2644\u001b[0m         resample=None, url=None, *, data=None, **kwargs):\n\u001b[1;32m-> 2645\u001b[1;33m     __ret = gca().imshow(\n\u001b[0m\u001b[0;32m   2646\u001b[0m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maspect\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m         \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1563\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1564\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1565\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1566\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1567\u001b[0m         \u001b[0mbound\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    356\u001b[0m                 \u001b[1;34mf\"%(removal)s.  If any parameter follows {name!r}, they \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m                 f\"should be pass as keyword, not positionally.\")\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    359\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mimshow\u001b[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)\u001b[0m\n\u001b[0;32m   5624\u001b[0m                               resample=resample, **kwargs)\n\u001b[0;32m   5625\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5626\u001b[1;33m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5627\u001b[0m         \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_alpha\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5628\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\image.py\u001b[0m in \u001b[0;36mset_data\u001b[1;34m(self, A)\u001b[0m\n\u001b[0;32m    691\u001b[0m         if (self._A.dtype != np.uint8 and\n\u001b[0;32m    692\u001b[0m                 not np.can_cast(self._A.dtype, float, \"same_kind\")):\n\u001b[1;32m--> 693\u001b[1;33m             raise TypeError(\"Image data of dtype {} cannot be converted to \"\n\u001b[0m\u001b[0;32m    694\u001b[0m                             \"float\".format(self._A.dtype))\n\u001b[0;32m    695\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: Image data of dtype object cannot be converted to float"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "channel1 = get_image_feature(p1, q1, s1, k)\n",
    "plt.imshow(channel1, interpolation='nearest')\n",
    "plt.title(\"channel:1 k=%d\" % k)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'channel2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-5cfe697eff28>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 扩展维度为3维\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mchannel1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchannel1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mchannel2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchannel2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mchannel3\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchannel3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mnew_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchannel1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchannel2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchannel3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'channel2' is not defined"
     ]
    }
   ],
   "source": [
    "# 扩展维度为3维\n",
    "channel1 = np.expand_dims(channel1, axis=-1)\n",
    "channel2 = np.expand_dims(channel2, axis=-1)\n",
    "channel3 = np.expand_dims(channel3, axis=-1)\n",
    "new_img = np.concatenate((channel1, channel2, channel3), axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_img' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-d3bb14eadece>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_img\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'nearest'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"k=%d\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'off'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'new_img' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "plt.imshow(new_img, interpolation='nearest')\n",
    "plt.title(\"k=%d\" % k)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对通道2进行奇异值分解"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'channel2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-2dc3f24fdaf7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# axis=2 方向合并\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnew_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchannel1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchannel2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchannel3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mnew_img\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'channel2' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "# axis=2 方向合并\n",
    "new_img = np.concatenate((channel1, channel2, channel3), axis=2)\n",
    "new_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'new_img' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-e8aba44664b8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# RGB要求数组为整数类型，因此修改数组类型\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnew_img\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_img\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'new_img' is not defined"
     ]
    }
   ],
   "source": [
    "# RGB要求数组为整数类型，因此修改数组类型\n",
    "new_img = np.uint8(new_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_img_ = Image.fromarray(new_img)\n",
    "new_img_.save('./new_img.jpg')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
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